AI systems and agentic workflows
Move AI from impressive to dependable.
Design RAG systems, MCP integrations, and agentic workflows around real users, trusted data, observable behaviour, security boundaries, and measurable outcomes.
Scope
- Use cases and workflow design
- Retrieval and knowledge architecture
- Tool and MCP integration
- Agent orchestration
- Evaluation and guardrails
- Human-in-the-loop design
- Security and observability
- Production deployment
In short: It only answers from your own documents, shows you where every answer came from, and asks before doing anything that matters.
- Your documentsThe manuals, contracts and files you already have.
- OrganisedEverything is split into small pieces that are easy to search.
- Right pieces foundFor each question, the most relevant passages are picked out.
- Answer writtenThe AI writes an answer using only those passages.
- Checked twiceRules check the answer; anything risky waits for a person.
- Answer with its sourceYou get the answer and a link to where it came from.
A worked example
Take a manufacturer that receives orders as emails with a PDF specification attached. Someone opens each one, reads the dimensions and quantities, checks nothing is missing, and types it all into the production system. Here is the same job with the typing taken out.
In short: The order is read and typed in for you; you only check it and approve.
- Email arrivesA customer sends an order with a PDF attached.
- Details readSizes, quantities and dates are pulled from the email and PDF.
- Gaps flaggedAnything missing or unclear is marked for you.
- You approveYou check the draft order and fix what's needed.
- Into your systemThe order goes straight into production, no retyping.
- Customer confirmedThe customer gets a confirmation automatically.
Today
10–20 minutes of reading, checking, and retyping for every order — and the mistakes that come with doing it at speed.
With the automation
1–2 minutes to review a draft that is already filled in, with the uncertain parts marked for you to decide.
This is an illustrative walkthrough, not a client result. The times are typical for orders handled by hand; what your company would actually save is one of the things the opportunity study is for.
Deliverables
- workflow and use-case definition tied to a business outcome
- RAG or agent architecture with data and integration plan
- evaluation strategy with concrete test sets
- security, guardrail, and observability plan
- a thin working vertical slice in your environment
Typical engagement
AI Production Readiness Sprint
For companies with an AI concept or prototype that needs a credible path to production.
Suggested duration: 2–4 weeks
Selected experience
AI
Enterprise RAG and agent knowledge platform
Designed a knowledge platform that turns internal documents, code, and operational knowledge into governed AI workflows, combining RAG, MCP tools, LangGraph orchestration, and LangSmith observability.
- RAG
- MCP
- LangGraph
- LangSmith
Product engineering
IoT and energy data platform
Designed data ingestion and analysis workflows for distributed meters and sensors, combining device integration, time-series data, automation, dashboards, anomaly detection, and AI-assisted insights.
- MQTT
- Time-series DB
- Node.js
- Automation
AI
ESP32 edge AI acoustic sensing node
Designed and built a field-ready ESP32-S3 edge device that runs acoustic ML inference locally, reports compact LoRaWAN telemetry, captures labelled training data, and gives operators a local web console for deployment and recovery.
- ESP32-S3
- ESP-IDF
- TensorFlow Lite Micro
- LoRaWAN
FAQ
Do we need an agent or ordinary workflow automation?
Often you need ordinary automation. Agents earn their place when the workflow genuinely requires judgement across steps — otherwise a deterministic pipeline is cheaper, faster, and easier to operate. Part of the first engagement is answering exactly this question honestly.
Can you work with our existing model provider?
Yes. The architecture is designed so the model provider is a replaceable component. Whether you use a hosted API, a cloud provider's models, or self-hosted open weights, the retrieval, tooling, evaluation, and guardrail layers stay the same.
How do you evaluate a RAG system?
With a test set built from real questions and documents, scored on retrieval quality and answer faithfulness separately. Evaluation is set up early so every retrieval or prompt change is measured, not eyeballed.
Can the system be self-hosted?
Yes. Self-hosting models, vector stores, and orchestration is a supported path — usually driven by data residency or procurement constraints. The trade-offs in cost and capability are documented before you commit.
How do you prevent an agent from accessing the wrong tools or data?
Tool access is scoped per workflow, not per model: each agent step gets an explicit allowlist, credentials with minimal permissions, input/output validation, and human approval gates where actions are irreversible. Access boundaries are part of the architecture, not a prompt instruction.
Curious what AI could actually do for your business?
Bring your questions, including the ones that feel too basic. In 30 minutes we go through how you work today, pick the task with the most to gain, and sketch what testing it would involve. No pitch, no obligation.
Prefer email? hello@nxtinno.com